DeepEMs-25: a deep-learning potential to decipher kinetic tug-of-war dictating thermal stability in energetic materials
Abstract Atomic-scale insight into decompositions in energetic materials (EMs) is essential for harnessing energy release, which remains elusive due to both instrumental and computational limitations. Herein, we developed DeepEMs-25, a deep-learning potential trained on diverse EMs towards accurate...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-025-01739-7 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849332279478845440 |
|---|---|
| author | Ming-Yu Guo Yun-Fan Yan Pin Chen Wei-Xiong Zhang |
| author_facet | Ming-Yu Guo Yun-Fan Yan Pin Chen Wei-Xiong Zhang |
| author_sort | Ming-Yu Guo |
| collection | DOAJ |
| description | Abstract Atomic-scale insight into decompositions in energetic materials (EMs) is essential for harnessing energy release, which remains elusive due to both instrumental and computational limitations. Herein, we developed DeepEMs-25, a deep-learning potential trained on diverse EMs towards accurate and efficient simulations. Applying DeepEMs‑25 to an isostructural ABX3 molecular perovskites series, with A-site organic cations, B-site alkali or ammonium cations, and X-site perchlorate anions, we probe the effect of cation size on reactivity. Arrhenius analysis of 100-ps trajectories reveals that increasing B‑site ionic radius simultaneously decreases X–A collision’s activation energy (enhancing reaction rates) and decreases X–A collision’s pre‑exponential factor (reducing collision frequency), producing opposing kinetic effects. Such “kinetic tug‑of‑war” explains why an intermediate‑sized cation yields maximal thermal stability by optimally balancing reactivity and collision dissipation. A similarly sized reactive cation promotes additional hydrogen-transfer pathways causing accelerating decomposition. Our findings link atomistic kinetics to macroscopic stability, informing next-generation EMs design. |
| format | Article |
| id | doaj-art-f29f7a7f36f043568a8930a8c19256fc |
| institution | Kabale University |
| issn | 2057-3960 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj-art-f29f7a7f36f043568a8930a8c19256fc2025-08-20T03:46:15ZengNature Portfolionpj Computational Materials2057-39602025-07-0111111010.1038/s41524-025-01739-7DeepEMs-25: a deep-learning potential to decipher kinetic tug-of-war dictating thermal stability in energetic materialsMing-Yu Guo0Yun-Fan Yan1Pin Chen2Wei-Xiong Zhang3MOE Key Laboratory of Bioinorganic and Synthetic Chemistry, School of Chemistry, IGCME, Sun Yat-sen UniversityMOE Key Laboratory of Bioinorganic and Synthetic Chemistry, School of Chemistry, IGCME, Sun Yat-sen UniversityNational Supercomputer Center in Guangzhou, School of Computer Science and Engineering, Sun Yat-sen UniversityMOE Key Laboratory of Bioinorganic and Synthetic Chemistry, School of Chemistry, IGCME, Sun Yat-sen UniversityAbstract Atomic-scale insight into decompositions in energetic materials (EMs) is essential for harnessing energy release, which remains elusive due to both instrumental and computational limitations. Herein, we developed DeepEMs-25, a deep-learning potential trained on diverse EMs towards accurate and efficient simulations. Applying DeepEMs‑25 to an isostructural ABX3 molecular perovskites series, with A-site organic cations, B-site alkali or ammonium cations, and X-site perchlorate anions, we probe the effect of cation size on reactivity. Arrhenius analysis of 100-ps trajectories reveals that increasing B‑site ionic radius simultaneously decreases X–A collision’s activation energy (enhancing reaction rates) and decreases X–A collision’s pre‑exponential factor (reducing collision frequency), producing opposing kinetic effects. Such “kinetic tug‑of‑war” explains why an intermediate‑sized cation yields maximal thermal stability by optimally balancing reactivity and collision dissipation. A similarly sized reactive cation promotes additional hydrogen-transfer pathways causing accelerating decomposition. Our findings link atomistic kinetics to macroscopic stability, informing next-generation EMs design.https://doi.org/10.1038/s41524-025-01739-7 |
| spellingShingle | Ming-Yu Guo Yun-Fan Yan Pin Chen Wei-Xiong Zhang DeepEMs-25: a deep-learning potential to decipher kinetic tug-of-war dictating thermal stability in energetic materials npj Computational Materials |
| title | DeepEMs-25: a deep-learning potential to decipher kinetic tug-of-war dictating thermal stability in energetic materials |
| title_full | DeepEMs-25: a deep-learning potential to decipher kinetic tug-of-war dictating thermal stability in energetic materials |
| title_fullStr | DeepEMs-25: a deep-learning potential to decipher kinetic tug-of-war dictating thermal stability in energetic materials |
| title_full_unstemmed | DeepEMs-25: a deep-learning potential to decipher kinetic tug-of-war dictating thermal stability in energetic materials |
| title_short | DeepEMs-25: a deep-learning potential to decipher kinetic tug-of-war dictating thermal stability in energetic materials |
| title_sort | deepems 25 a deep learning potential to decipher kinetic tug of war dictating thermal stability in energetic materials |
| url | https://doi.org/10.1038/s41524-025-01739-7 |
| work_keys_str_mv | AT mingyuguo deepems25adeeplearningpotentialtodecipherkinetictugofwardictatingthermalstabilityinenergeticmaterials AT yunfanyan deepems25adeeplearningpotentialtodecipherkinetictugofwardictatingthermalstabilityinenergeticmaterials AT pinchen deepems25adeeplearningpotentialtodecipherkinetictugofwardictatingthermalstabilityinenergeticmaterials AT weixiongzhang deepems25adeeplearningpotentialtodecipherkinetictugofwardictatingthermalstabilityinenergeticmaterials |